Monitoring Health Changes in Congestive Heart Failure Patients Using Wearables and Clinical Data

In this work we present systems to monitor the health of a wide variety of high risk patients living with Congestive Heart Failure. For critical hospitalized patients, we introduce a deep learning framework for hospital records that uses a Word2Vec vector space representation to learn from a combination of structured data and unstructured text. The deep learning framework is able to assess patient risk, and accurately predict medical outcomes into the future. For less critical patients living at home, we also present algorithms for remote monitoring that can track a patient's changing health indicators using a wearable heart-rate sensors. The pool of individuals living with congestive heart failure is very diverse, which can make multifaceted approaches to health monitoring, such as those presented in this work, attractive for observing large pools of high-risk patients. Collectively the methods presented in this paper allow us to continuously monitor a person living with CHF through hospitalization, discharge, and into their home.